Formation pressure prediction method and device

Abstract

The present invention provides a method for predicting formation pressure, including: S101: performing preprocessing and well logging interpretation based on incomplete well logging data; S102: jointly using a rock physics model, an empirical formula, and a shallow compaction trend to complement the elastic parameter data and density data of the formation in the entire well section; S103: calculating the overburden pressure based on the density data of the formation in the entire well section; S104: calculating the hydrostatic pressure according to the formation water properties of the formation in the entire well section; S105: constructing normal compaction trend data according to a selected compaction model and based on the overburden pressure and the hydrostatic pressure; S106: calculating the effective stress according to a selected pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the formation in the entire well section, the overburden pressure, and the hydrostatic pressure; S107: predicting the formation pressure according to the overburden pressure and the effective stress. The present invention also provides a device for predicting formation pressure.

Description

Technical Field

[0001] The present invention relates to the field of formation pressure prediction, in particular to a formation pressure prediction method, and more particularly to a formation pressure prediction method under incomplete data conditions. The present invention also relates to a formation pressure prediction device. Background Art

[0002] Formation pressure prediction is an important part of in-situ stress evaluation. The accuracy of formation pressure prediction plays an important role in the evaluation and development of oil and gas reservoirs, and directly affects the selection of drilling mud density, casing type, etc.

[0003] In common pressure prediction methods, density data and velocity data are crucial. The incompleteness or unrefined processing of data will increase the uncertainty of destination formation pressure prediction and bring great risks to drilling. The common method is to supplement the missing data according to empirical formulas. On the one hand, many widely used empirical formulas are only applicable to the work areas where the empirical formulas are constructed. On the other hand, an empirical formula often only applies to the data of a certain formation series, while formation pressure prediction requires curve data such as velocity and density of the whole well section. The unrefined predicted density data will greatly affect the calculation of overburden formation pressure, and thus affect the construction of the subsequent normal compaction trend line and formation pressure prediction.

[0004] In view of this, there is a current need to provide a formation pressure prediction method under incomplete data conditions.

[0005] The above description is only for understanding the relevant technologies in the field and does not admit that it belongs to the prior art. Summary of the Invention

[0006] The present invention aims to provide a formation pressure prediction method under incomplete data conditions.

[0007] In an embodiment of the present invention, a formation pressure prediction method is provided, including:

[0008] S101: Perform preprocessing and well logging interpretation based on incomplete well logging data;

[0009] S102: Jointly use a rock physics model, empirical formulas, and shallow compaction trends to complete the elastic parameter data and density data of the whole well section formation;

[0010] S103: Calculate the overburden formation pressure based on the density data of the whole well section formation;

[0011] S104: Calculate the hydrostatic pressure according to the formation water properties of the whole well section formation;

[0012] S105: Construct normal compaction trend data according to the selected compaction model and based on the overburden formation pressure and the hydrostatic pressure;

[0013] S106: Calculate the effective stress according to the selected pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the entire wellbore formation, the overburden pressure, and the hydrostatic pressure;

[0014] S107: Predict the formation pressure according to the overburden pressure and the effective stress.

[0015] In some embodiments, the formation pressure prediction method further includes:

[0016] S100: Obtain logging data and determine whether the logging data is complete according to whether it includes the elastic parameter data and density data of the entire wellbore formation.

[0017] In some embodiments, step S101 includes:

[0018] Perform environmental correction and normalization processing on the logging data;

[0019] Perform logging interpretation;

[0020] Determine the rock physics model based on the results of the logging interpretation;

[0021] Based on the results of the logging interpretation and combined with regional understanding, identify the special lithology formations in the entire wellbore formation.

[0022] In some embodiments, the performing logging interpretation includes:

[0023] According to the available lithology and physical property curves and / or cuttings logging data, obtain the mineral composition of the mixed minerals associated with the rock physics model and the porosity and saturation curves of the pores.

[0024] In some embodiments, the elastic parameter is velocity, impedance, or density.

[0025] In some embodiments, step S102 includes:

[0026] Use the rock physics model to obtain the velocity data and / or density data of the target formation in the entire wellbore formation;

[0027] Based on only obtaining one of the velocity data or density data of the target formation using the rock physics model, use the empirical formula to obtain the other one;

[0028] Use the rock physics model and the empirical formula to obtain the velocity data and density data of the special lithology formations in the entire wellbore formation;

[0029] Based on the geological understanding of the shallow formation in the whole well section and using the measured curves in the adjacent area, the velocity data and density data of the shallow formation are obtained.

[0030] In some embodiments, the obtaining of the velocity data and / or density data of the target formation in the whole well section by using the rock physics model includes:

[0031] Calculating the elastic modulus of the mixed minerals by using the Voigt-Reuss-Hill average model;

[0032] Embedding the pores into the mixed minerals by using the differential equivalent medium model (DEM) to calculate the bulk modulus and shear modulus of the dry rock skeleton;

[0033] Mixing the gas phase and liquid phase fluids in the pores by using the Wood equation or the patchy saturation model to calculate the bulk modulus of the mixed fluid;

[0034] Adding the mixed fluid into the pores by using the Gassmann equation to calculate the bulk modulus and shear modulus of the saturated rock, so as to obtain the velocity data and density data of the target formation.

[0035] In some embodiments, the empirical formula is a density-velocity empirical formula for calculating density data or velocity data.

[0036] In some embodiments, the density-velocity empirical formula is in polynomial form or exponential form.

[0037] In some embodiments, the compaction model is a compaction model based on the velocity-effective stress relationship.

[0038] In some embodiments, the compaction model is a compaction model based on the velocity-depth relationship.

[0039] In some embodiments, the pressure prediction model is a pressure prediction model based on the Eaton method

[0040] In some embodiments, the pressure prediction model is a pressure prediction model based on the Bowers method.

[0041] In some embodiments, the formation pressure prediction method further includes:

[0042] Judging whether the error between the prediction result of the formation pressure and the engineering parameters is within a predetermined range;

[0043] If not, repeat steps S105 to S107 until the error is within the predetermined range.

[0044] In an embodiment of the present invention, a formation pressure prediction device is provided, including:

[0045] A preprocessing and logging interpretation unit configured to perform preprocessing and logging interpretation based on incomplete logging data;

[0046] A data completion unit configured to jointly utilize a petrophysical model, empirical formulas, and shallow compaction trends to complete elastic parameter data and density data of formations in the entire well section;

[0047] A first calculation unit configured to calculate overburden pressure based on the density data of the formations in the entire well section;

[0048] A second calculation unit configured to calculate hydrostatic pressure according to the formation water properties of the formations in the entire well section;

[0049] A normal compaction trend data construction unit configured to construct normal compaction trend data according to a selected compaction model and based on the overburden pressure and the hydrostatic pressure;

[0050] An effective stress calculation unit configured to calculate effective stress according to a selected pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the formations in the entire well section, the overburden pressure, and the hydrostatic pressure;

[0051] A formation pressure prediction unit configured to predict the formation pressure according to the overburden pressure and the effective stress.

[0052] Thus, by means of the formation pressure prediction method according to the embodiments of the present invention, data of the target formation are obtained at least through a petrophysical model, and then data of formations with special lithologies are obtained in combination with empirical formulas, and data of shallow formations are obtained by using curves of adjacent areas; thus, missing data in the formations of the entire well section are supplemented by a comprehensive method of jointly utilizing a petrophysical model, empirical formulas, and shallow compaction trends, overcoming the incompleteness of data for predicting formation pressure. Based on this, by adopting appropriate compaction models and pressure prediction models, the accuracy and rationality of the formation pressure prediction results are improved. The formation pressure prediction results obtained according to the present invention have important practical application values in subsequent engineering applications.

[0053] Compared with the technical solutions provided by the embodiments of the present invention, the formation pressure prediction methods provided by some known techniques of inventors only supplement missing data according to empirical formulas. The data obtained based on this are difficult to be applicable to the entire work area or all formations, so this kind of data lacks accuracy and rationality, and is very likely to affect the calculation of overburden pressure, and further affect the construction of the normal compaction trend line and the formation pressure prediction results.

[0054] Other features and advantages of the embodiments of the present invention can be learned from the following specific embodiments, and some can be deduced by those skilled in the art through the teachings herein. Description of the Drawings

[0055] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings, where:

[0056] Figure 1 shows a flowchart of a formation pressure prediction method according to an embodiment of the present invention;

[0057] Figure 2 shows the original logging curves of Well A according to an embodiment of the present invention;

[0058] Figure 3 shows the predicted density curve of the entire well section formation according to an embodiment of the present invention;

[0059] Figure 4 shows the overburden pressure and hydrostatic pressure according to an embodiment of the present invention;

[0060] Figure 5 shows the comparison between the trend line and the measured curve of the formation pressure prediction result of Well A according to an embodiment of the present invention;

[0061] Figure 6 shows a formation pressure prediction device according to an embodiment of the present invention. Detailed Embodiments

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be described in detail below with reference to the specific embodiments and the drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

[0063] In an embodiment of the present invention, a formation pressure prediction method is provided, specifically a formation pressure prediction method under incomplete data. Through this method, the completeness of the data is improved, and thus the accuracy and rationality of the formation pressure prediction are improved.

[0064] In some embodiments, under formation sealing conditions, the overburden pressure is jointly borne by the particulate matter of the rock and the fluid in the pores, as shown in the following formula:

[0065] P ov =P p +P e Equation ①

[0066] In Equation ①, P ov is the overburden pressure, P e is the effective pressure, i.e., the pressure borne by the rock skeleton, and P p is the formation pore pressure, that is, the formation pressure to be predicted.

[0067] The widely used formation pressure prediction method in the industry is based on the effective stress theory expressed as Equation ①. Its theoretical basis is that elastic parameters, such as seismic velocity (hereinafter referred to as velocity for short), increase with the increase of effective stress. This pressure dependence comes from the closure of particle contacts, micropores, and fractures, making the rock mineral skeleton harder; by increasing pore pressure by opening particle contacts and micropores, etc., the rock mineral skeleton becomes softer, thereby reducing the seismic velocity. This theory has been verified in experimental measurements. By comparing the relationship between the velocity and pressure measured in the laboratory, it can be found that there is a good positive correlation between velocity-effective stress, and this relationship lays the foundation for predicting formation pressure using elastic parameters including velocity.

[0068] The core of pressure prediction based on the above effective stress theory includes three aspects: (1) calculating the overburden pressure; (2) constructing a normal pressure trend line, that is, the curve or data volume of elastic parameters under normal compaction conditions; (3) establishing the relationship between elastic parameters, such as velocity and effective stress, that is, the degree to which the elastic parameters, such as velocity, are affected by effective stress. To achieve these three aspects, the support of necessary data is required. Usually, elastic parameter data, such as velocity data and density data, are necessary. In practical applications, necessary data are often missing, especially in newly explored areas. Therefore, overcoming the incompleteness of data is a very important content in formation pressure prediction.

[0069] The incompleteness of data exists in different situations. A common situation is that there is velocity data in the study area but density data is missing. In this case, the common method to obtain density data is to use velocity data to obtain density data according to empirical formulas. The main problem with the empirical formula method is that it is impossible to quality control the accuracy of density data; another situation is that there is neither velocity data nor density data in the study area, but some lithology curves and physical property curves are available. In this case, the empirical formula is powerless. The present invention proposes a comprehensive method that combines rock physics models, empirical formulas, and shallow compaction trends to overcome data incompleteness for the above-mentioned problems.

[0070] Specifically, in the Figure 1 illustrated embodiment, the method may include the following steps S101 to S107.

[0071] S101: Perform preprocessing and well logging interpretation based on incomplete well logging data.

[0072] In some embodiments, the well logging data may include but are not limited to spontaneous potential (SP) well logging curves, natural gamma (GR) well logging curves, caliper (CAL) well logging curves, acoustic travel time (AC) well logging curves, density (DEN) well logging curves, neutron (CNL) well logging curves, and deep / medium / shallow induction resistivity (RILD / RILM / RILS) well logging curves.

[0073] In some embodiments, step S101 may include:

[0074] A1: Perform environmental correction and normalization processing on the logging data.

[0075] By way of explanation and not limitation, the logging data and curves may be depth-aligned.

[0076] Based on the preprocessing of the logging data, provide better-quality basic data for subsequent processing.

[0077] A2: Conduct logging interpretation.

[0078] In some embodiments, logging interpretation refers to determining the relationship applied between logging data or logging information and geological information, and processing the logging data or logging information into geological information using the correct method.

[0079] In a specific embodiment, the logging interpretation may be to obtain the mineral composition of the mixed minerals associated with the rock physics model for complementing the elastic parameter data and density data of the whole well section formation, and the porosity and saturation curves of the pores, based on the available lithology and physical property curves and / or cuttings logging data.

[0080] A3: Determine the rock physics model based on the results of the logging interpretation.

[0081] In some embodiments, determine which rock physics model to adopt based on the mineral composition of the mixed minerals and the porosity and saturation curves of the pores obtained from the logging interpretation.

[0082] In some embodiments, the rock physics model is used to predict the elastic parameter data and / or density data of the target formation in the whole well section formation.

[0083] In some embodiments, the results obtained from the logging interpretation are used as the input and / or parameters of the rock physics model.

[0084] A4: Based on the results of the logging interpretation and combined with regional understanding, identify the special lithology formations in the whole well section formation.

[0085] In some embodiments, the special lithology formation is located above the target formation.

[0086] In some embodiments, the formation pressure prediction method further includes:

[0087] S100: Obtain logging data and determine whether the logging data is complete according to whether it includes the elastic parameter data and density data of the whole well section formation.

[0088] In some embodiments, when it is determined that the well logging data is incomplete, step S101 is executed.

[0089] In some embodiments, for predicting formation pressure, the lack of either the elastic parameter data or the density data can be regarded as incomplete well logging data.

[0090] In some embodiments, for example, for the target formation, it is common that there is elastic parameter data but lack of density data.

[0091] In some embodiments, for example, for the target formation, it is also possible that there is density data but lack of elastic parameter data.

[0092] In some embodiments, for example, for the special lithology formation, there is a lack of both elastic parameter data and density data.

[0093] In the embodiments of the present invention, the elastic parameters for predicting formation pressure can be velocity, impedance or density. Among them, in some embodiments, velocity data is often used for formation pressure prediction. In other embodiments, parameter data such as impedance or density can also be used for formation pressure prediction.

[0094] S102: Jointly use the rock physics model, empirical formula and shallow compaction trend to complete the elastic parameter data and density data of the formation in the entire well section.

[0095] In some embodiments and as described above, to predict the pressure of the target formation in the formation of the entire well section, the elastic parameter data and density data of the formation in the entire well section are required.

[0096] In some embodiments, the formation of the entire well section is composed of the target formation, special lithology formation and shallow formation.

[0097] Next, taking the elastic parameter data as velocity data as an example for further illustration.

[0098] In some embodiments, step S102 includes:

[0099] B1: Use the rock physics model to obtain the velocity data and / or density data of the target formation in the formation of the entire well section.

[0100] In some embodiments, in practical applications, different rock physics models are usually required according to different lithologies.

[0101] In some embodiments, step B1 includes:

[0102] C1: Use the Voigt-Reuss-Hill average model to calculate the elastic modulus of the mixed minerals.

[0103] In some embodiments, information about the mixed minerals, such as their mineral components, is obtained through well logging interpretation.

[0104] C2: Use the differential equivalent medium model (DEM) to embed the pores into the mixed minerals to calculate the bulk modulus and shear modulus of the dry rock skeleton.

[0105] In some embodiments, information about the pores, such as their porosity, saturation curve, etc., is obtained through well logging interpretation.

[0106] C3: Use Wood's equation or the patchy saturation model to mix the gas phase and liquid phase fluids in the pores to calculate the bulk modulus of the mixed fluid;

[0107] C4: Use Gassmann's equation to add the mixed fluid to the pores to calculate the bulk modulus and shear modulus of the saturated rock, thereby obtaining the velocity data and density data of the target formation.

[0108] Through the above steps, the velocity data and density data of the target formation are obtained using the rock physics model.

[0109] In some embodiments, the velocity data includes the P-wave velocity and the S-wave velocity.

[0110] B2: Based on obtaining only one of the velocity data or density data of the target formation using the rock physics model, use the empirical formula to obtain the other one.

[0111] In some embodiments, for example, due to the lack of original data, only one of the velocity data or density data of the target formation can be obtained through the rock physics model. In this case, it is necessary to combine the empirical formula to predict the other one.

[0112] B3: Use the rock physics model and the empirical formula to obtain the velocity data and density data of the special lithology formation in the entire well section.

[0113] In some embodiments, for the special lithology formation, the results of well logging interpretation may not be able to fully and accurately reflect the composition of the rock. Therefore, it is necessary to combine the empirical formula to predict the missing data or curves.

[0114] In some embodiments, there are many empirical relationships between elastic parameters. For formation pressure prediction, the commonly used density-velocity empirical formula is used. Through the density-velocity empirical formula, density data can be predicted from velocity data, and velocity data can also be predicted from density data.

[0115] In some embodiments, the density-velocity empirical formula can be in polynomial form:

[0116]

[0117] In some embodiments, the density-velocity empirical formula may be in exponential form:

[0118]

[0119] In Equations ② and ③, a, b, c and d, f are formula coefficients, and different lithologies and different geological conditions have different coefficient values. V P is the P-wave velocity in the velocity data, and ρ is the density.

[0120] B4: Based on the geological understanding of the shallow formations in the entire well section and using the measured curves in the adjacent area, obtain the velocity data and density data of the shallow formations.

[0121] In some embodiments, in actual operation, logging operations are usually rarely performed on the shallow formations in the entire well section, resulting in very little data or curves or no data or curves available for the shallow formations. In this case, it is necessary to combine the regional trend, i.e., the shallow compaction trend, to supplement the missing necessary data or curves.

[0122] In some embodiments, the elastic parameter data of the adjacent area, such as velocity data and density data, are used as the elastic parameter data of the shallow formations in the entire well section, such as velocity data and density data. Among them, in some embodiments, the data in the adjacent area can be appropriately processed and used as the velocity data and density data of the shallow formations.

[0123] So far, through the above step S102, the velocity data and density data of the target formation, special lithology formation and shallow formation have been obtained by jointly using the rock physics model, empirical formula and shallow compaction trend, that is, the velocity data and density data of the entire well section have been complemented.

[0124] After supplementing the necessary data for formation pressure prediction, formation pressure prediction can be carried out according to the effective stress theory.

[0125] S103: Calculate the overburden pressure based on the density data of the entire well section.

[0126] In some embodiments, calculate the overburden pressure energy according to the density data of the entire well section or the entire depth. An exemplary formula is as follows:

[0127]

[0128] In Equation ④, H is the depth, ρ is the density data, and g is the acceleration due to gravity.

[0129] S104: Calculate the hydrostatic pressure according to the formation water properties of the whole well section formation.

[0130] The exemplary formula is as follows:

[0131] P h = ρ w ·g·h Formula ⑤

[0132] In Formula ⑤, P h is the hydrostatic pressure, ρ w is the formation water density, g is the acceleration of gravity, and h is the water column height.

[0133] S105: Construct normal compaction trend data based on the selected compaction model and based on the overburden pressure and the hydrostatic pressure.

[0134] After calculating the overburden pressure and the hydrostatic pressure, the normal compaction trend data, also known as the normal compaction trend line, can be constructed through the compaction model.

[0135] In some embodiments, the normal compaction data is velocity data, also known as the trend velocity.

[0136] In some embodiments, the compaction model can be based on the velocity-effective stress relationship.

[0137] In some embodiments, the compaction model can be based on the velocity-depth relationship.

[0138] In the embodiments of the present invention, the velocity-effective stress relationship is taken as an example of the compaction model for illustration. The normal compaction trend data constructed through the velocity-effective stress relationship can be exemplarily expressed as:

[0139]

[0140] In Formula ⑥, v normal is the trend velocity under the normal compaction trend; v 0 is the velocity under zero effective stress, which can be an empirical value; σ normal is the normal effective stress under the normal compaction trend; A and B are formula parameters.

[0141] In Formula ⑥, the normal effective stress is obtained by calculating the difference between the overburden pressure and the hydrostatic pressure:

[0142] σ normal = P ov - P h Formula ⑦

[0143] S106: Calculate the effective stress according to the selected pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the entire well section formation, the overburden pressure, and the hydrostatic pressure.

[0144] After obtaining the normal compaction trend data, a pressure prediction model applicable to the study area or work area can be adopted, and the corresponding effective stress can be calculated using elastic parameter data, such as velocity data.

[0145] In some embodiments, the pressure prediction model is based on the Eaton method.

[0146] In some embodiments, the pressure prediction model is based on the Bowers method.

[0147] In the embodiments of the present invention, taking the pressure prediction model based on the Eaton method as an example for illustration, it can be expressed as:

[0148] σ = σ normal (v / v normal ) n Equation ⑧

[0149] In Equation ⑧, v is the velocity data of the entire well section formation; n is a parameter describing the sensitivity of velocity to effective stress.

[0150] In some embodiments, in practical applications, the parameter n needs to be constrained by measured pressure.

[0151] S107: Predict the formation pressure according to the overburden pressure and the effective stress.

[0152] Combining Equation ⑧ and Equation ①, the formation pressure to be predicted can be expressed as:

[0153] P p = P ov - (P ov - P h )(v / v normal ) n Equation ⑨

[0154] In some embodiments, the formation pressure prediction method further includes:

[0155] Judge whether the error between the predicted result of the formation pressure and the engineering parameters is within a predetermined range;

[0156] If not, repeat steps S105 to S107 until the error is within the predetermined range.

[0157] In some embodiments, the trend line of the predicted formation pressure is compared with engineering parameters. If the error between the trend line and the engineering parameters is within a predetermined range, the predicted result of the formation pressure is considered reasonable. If the error is not within the predetermined range, steps S105 to S107 are repeated until the error is within the predetermined range, indicating that the predicted result of the formation pressure can reflect the true formation pressure.

[0158] In some embodiments, the predetermined range of the error can be determined according to the actual situation. By way of explanation and not limitation, it can be 5%, 10%, 20%, 30%, or other percentages.

[0159] In some embodiments, the following method can also be used to determine whether the predicted result of the formation pressure reflects the actual situation: A certain number of engineering parameter sample points are taken and compared with the trend line of the predicted formation pressure respectively. If the number of engineering parameter sample points with an error within the predetermined range from the trend line reaches a certain proportion in the total number, it is considered that the predicted result of the formation pressure can reflect the true formation pressure.

[0160] In some embodiments, the proportion can be 60%, 70%, 80%, 90% or other percentages.

[0161] In some embodiments, the engineering parameters can be measured pressure point data or mud density data.

[0162] In some embodiments, in order to conduct comparative analysis with engineering parameters such as mud density, it is usually necessary to calculate the formation pressure coefficient, which is the ratio of the formation pressure to the hydrostatic pressure.

[0163] Next, refer to Figures 2 to 5 , and the formation pressure prediction method of the embodiments of the present invention will be described in combination with Well A.

[0164] In Figure 2 the logging data of Well A is shown, that is, the original logging curves, including the caliper (CAL) logging curve, the natural gamma (GR) logging curve, the spontaneous potential (SP) logging curve, and the acoustic travel time (AC) logging curve. Based on these original logging curves, it can be judged that at least density data is missing. Therefore, for formation pressure prediction, the logging data is incomplete.

[0165] In Figure 3Shown therein are the density data of the formation across the entire well section obtained by a comprehensive method that jointly utilizes a rock physics model, empirical formulas, and shallow compaction trends. Among them, the density data of the shallow formation is obtained by combining the regional trend with the measured curves in the adjacent area. The density data of the middle formation, which is the special lithology formation in this embodiment, is obtained by empirical formulas. The density data of the deep formation, which is the target formation in this embodiment, is obtained by the rock physics model. The three are combined to obtain the density data of the formation across the entire well section / at the full depth.

[0166] In Figure 4 shown are the overburden pressure calculated based on the obtained density data and the hydrostatic pressure calculated based on the formation water properties, such as the formation water density curve.

[0167] In Figure 5 shown respectively are the comparison between the trend lines (the oblique straight lines in the figure) of the predicted P-wave velocity data and the predicted formation pressure and the measured curves. It can be seen that the trend line of the predicted formation pressure is in good agreement with the measured data and geological understanding, and can play a guiding role in the drilling engineering.

[0168] In an embodiment of the present invention, a formation pressure prediction device is provided.

[0169] In the embodiment as Figure 6 shown, the formation pressure prediction device 600 may include: a preprocessing and well logging interpretation unit 610 configured to perform preprocessing and well logging interpretation based on incomplete well logging data; a data completion unit 620 configured to jointly utilize a rock physics model, empirical formulas, and shallow compaction trends to complete the elastic parameter data and density data of the formation across the entire well section; a first calculation unit 630 configured to calculate the overburden pressure based on the density data of the formation across the entire well section; a second calculation unit 640 configured to calculate the hydrostatic pressure according to the formation water properties; a normal compaction trend data construction unit 650 configured to construct normal compaction trend data using a compaction model; an effective stress calculation unit 660 configured to calculate the effective stress using a pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the formation across the entire well section, the overburden pressure, and the hydrostatic pressure; and a formation pressure prediction unit 670 configured to predict the formation pressure according to the overburden pressure and the effective stress.

[0170] In some embodiments, the device may incorporate the method features of any embodiment, and vice versa, which will not be elaborated herein.

[0171] The methods, programs, systems, devices, etc. of the embodiments of the present invention can be executed or implemented in a single or multiple networked computers, and can also be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks can be executed by remote processing devices connected through a communication network.

[0172] Those skilled in the art should understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, those skilled in the art can think that the implementation of the functional modules / units or controllers and related method steps clarified in the above embodiments can be achieved in a software, hardware, or a combination of software and hardware manner.

[0173] In this document, multiple embodiments of the present invention are described. However, for the sake of brevity, the descriptions of each embodiment are not exhaustive, and the same or similar features or parts between the embodiments may be omitted. In this document, the specific features, structures, materials, or characteristics of each embodiment can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0174] In this document, the term "comprising", "including" or their variants are intended to be inclusive, not exhaustive, so that a process, method, product or device including a series of elements may include these elements, and does not exclude other elements that are not explicitly listed.

[0175] The exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are only examples of the best mode for implementing the systems and methods. Those skilled in the art can understand that various changes can be made to the embodiments of the systems and methods described here when implementing the systems and / or methods without departing from the spirit and scope of the present invention defined in the appended claims. The appended claims are intended to define the scope of the systems and methods, so the systems and methods falling within these claims and their equivalents can be covered. The above description of the systems and methods should be understood to include the combination of all the new and non-obvious elements described here, and there may be claims in this application or subsequent applications that involve the combination of any new and non-obvious elements. In addition, the above embodiments are exemplary, and among all the possible features and element combinations that can be claimed in this application or subsequent applications, no single feature or element is essential.

Claims

1. A formation pressure prediction method, including: S101: Perform preprocessing and well logging interpretation based on incomplete well logging data; S102: Jointly utilize petrophysical models, empirical formulas, and shallow compaction trends to complete the elastic parameter data and density data of the formation in the entire well section. The empirical formula is a density-velocity empirical formula for calculating density data or velocity data; S103: Calculate the overburden pressure based on the density data of the formation in the entire well section; S104: Calculate the hydrostatic pressure according to the formation water properties of the formation in the entire well section; S105: Construct normal compaction trend data according to the selected compaction model and based on the overburden pressure and the hydrostatic pressure; S106: Calculate the effective stress according to the selected pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the formation in the entire well section, the overburden pressure, and the hydrostatic pressure; S107: Predict the formation pressure according to the overburden pressure and the effective stress; The step S102 includes: Obtain the velocity data and / or density data of the target formation in the formation in the entire well section by using the petrophysical model; Based on only obtaining one of the velocity data or density data of the target formation by using the petrophysical model, obtain the other one by using the empirical formula; Obtain the velocity data and density data of the special lithology formation in the formation in the entire well section by using the petrophysical model and the empirical formula; Based on the geological understanding of the shallow formation in the entire well section and by using the measured curves in the adjacent area, obtain the velocity data and density data of the shallow formation; Among them, obtaining the velocity data and / or density data of the target formation in the formation in the entire well section by using the petrophysical model includes: Calculate the elastic modulus of the mixed minerals by using the Voigt-Reuss-Hill average model; Embed the pores into the mixed minerals by using the differential equivalent medium model (DEM) to calculate the bulk modulus and shear modulus of the dry rock skeleton; Mix the gas phase and liquid phase fluids in the pores by using the Wood equation or the patchy saturation model to calculate the bulk modulus of the mixed fluid; Add the mixed fluid into the pores by using the Gassmann equation to calculate the bulk modulus and shear modulus of the saturated rock, so as to obtain the velocity data and density data of the target formation.

2. The formation pressure prediction method according to claim 1, characterized in that it further includes: S100: Obtain well logging data and judge whether the well logging data is complete according to whether it includes the elastic parameter data and density data of the formation in the entire well section.

3. The formation pressure prediction method according to claim 1, characterized in that the step S101 includes: Perform environmental correction and standardization processing on the well logging data; Perform well logging interpretation; Determine the petrophysical model based on the results of the well logging interpretation; Based on the results of the well logging interpretation and combined with regional understanding, identify the special lithology formation in the entire well section.

4. The formation pressure prediction method according to claim 3, characterized in that the performing well logging interpretation includes: Based on the available lithology and physical property curves and / or cuttings logging data, obtain the mineral composition of the mixed minerals associated with the rock physics model and the porosity and saturation curves of the pores.

5. The formation pressure prediction method according to claim 4, characterized in that the elastic parameter is velocity, impedance or density.

6. The formation pressure prediction method according to claim 5, characterized in that the density-velocity empirical formula is in polynomial form or exponential form; The polynomial form is: where ρ is the density and V P is the longitudinal wave velocity in the velocity data, and a, b, and c are formula coefficients; The exponential form is: where d and f are formula coefficients.

7. The formation pressure prediction method according to claim 5, characterized in that the compaction model is a compaction model based on the velocity-effective stress relationship.

8. The formation pressure prediction method according to claim 5, characterized in that the compaction model is a compaction model based on the velocity-depth relationship.

9. The formation pressure prediction method according to claim 7 or 8, characterized in that the pressure prediction model is a pressure prediction model based on the Eaton method.

10. The formation pressure prediction method according to claim 7 or 8, characterized in that the pressure prediction model is a pressure prediction model based on the Bowers method.

11. The formation pressure prediction method according to claim 1, characterized in that further comprising: judging whether the error between the prediction result of the formation pressure and the engineering parameters is within a predetermined range; if not, repeat steps S105 to S107 until the error is within the predetermined range.

12. A formation pressure prediction device, characterized in that comprising: A preprocessing and well logging interpretation unit configured to perform preprocessing and well logging interpretation based on incomplete well logging data; A data completion unit configured to jointly utilize a rock physics model, an empirical formula, and a shallow compaction trend to complete the elastic parameter data and density data of the formation in the entire well section, and the empirical formula is a density-velocity empirical formula for calculating density data or velocity data; A first calculation unit configured to calculate the overburden pressure based on the density data of the formation in the entire well section; A second calculation unit configured to calculate the hydrostatic pressure according to the formation water properties of the formation in the entire well section; A normal compaction trend data construction unit configured to construct normal compaction trend data according to a selected compaction model and based on the overburden pressure and the hydrostatic pressure; An effective stress calculation unit configured to calculate the effective stress according to a selected pressure prediction model and based on the normal compaction trend data, the elastic parameter data of the formation in the entire well section, the overburden pressure, and the hydrostatic pressure; A formation pressure prediction unit configured to predict the formation pressure according to the overburden pressure and the effective stress; The data completion unit includes: Obtain the velocity data and / or density data of the target formation in the formation of the entire well section by using the rock physics model; Based on only obtaining one of the velocity data or density data of the target formation by using the rock physics model, obtain the other by using the empirical formula; Obtain the velocity data and density data of the special lithology formations in the whole well section by using the petrophysical model and the empirical formula; Based on the geological understanding of the shallow formations in the whole well section and by using the measured curves in the adjacent areas, obtain the velocity data and density data of the shallow formations; Among them, the obtaining of the velocity data and / or density data of the target formations in the whole well section by using the petrophysical model includes: Calculate the elastic modulus of the mixed minerals by using the Voigt-Reuss-Hill average model; Embed the pores into the mixed minerals by using the differential equivalent medium model (DEM) to calculate the bulk modulus and shear modulus of the dry rock skeleton; Mix the gas phase and liquid phase fluids in the pores by using the Wood equation or the patchy saturation model to calculate the bulk modulus of the mixed fluid; Add the mixed fluid into the pores by using the Gassmann equation to calculate the bulk modulus and shear modulus of the saturated rock, so as to obtain the velocity data and density data of the target formations.

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